Oceanic Nino Index Research Articles

Statistical Modeling of Impacts El Niño Southern Oscillations (ENSO) on Land Surface Temperature in Small Medium Size City: Case Study Kuching Sarawak

El Niño Southern Oscillation (ENSO) can affect the daily temperature and the amount of rainfall and extreme weather such as floods and droughts. For that reason, scientists need to understand the process of developing ENSO and develop statistical models to predict the impact of ENSO to land surface temperature. The remote sensing data provide spatial information that allows analyzing the influence of ENSO on land surface temperature spatial patterns. This study examines the ability of remote sensing data to study and develop model statistical for predicting the ENSO effect on land surface temperature spatial patterns. Remote sensing data needs to go through a pre-process and digital Number conversion to Land Surface Temperature (LST). To ensure accurate remote sensing information, the calibration process is carried out using temperature records from the Meteorological Malaysia Department (MMD). The next step is to conduct a correlation analysis between LST and Oceanic Niño Index (ONI). The final step is to use linear regression in building a statistical model forecasting the influence of ENSO on temperature and LST. The result found that changes in ONI values influence the value of LST and temperature. Improving knowledge and understanding of ENSO can provide ideas and strategies in reducing and adapting to the impact of ENSO on human beings.

Wet-environment Evapotranspiration and Precipitation Standardized Index (WEPSI) for drought assessment and monitoring

Abstract Drought assessment and monitoring are essential for its proper management. Drought indices play a fundamental role in this. This research introduces the Wet-environment Evapotranspiration and Precipitation Standardized Index (WEPSI) for drought assessment and monitoring. WEPSI incorporates water supply and demand into the drought index calculation. WEPSI considers precipitation (P) for water supply and wet-environment evapotranspiration (ETw) for water demand. We use an asymmetric complementary relationship to calculate ETw with actual (ETa) and potential evapotranspiration (ETp). WEPSI is tested in the transboundary Lempa River basin in the Central American dry corridor. ETw is estimated based on evapotranspiration data calculated using the Water Evaluation And Planning (WEAP) system hydrological model. To investigate the performance of WEPSI, we compare it with two well-known meteorological indices (Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index), together with a hydrological index (Standardized Runoff Index), in terms of statistical metrics and mutual information (MI). We compare WEPSI-derived droughts and historical information, including crop production, cereal yield, and the Oceanic Nino Index (ONI). Results show WEPSI has the highest correlation and MI, and the lowest deviation. It is consistent with the records of the crop production index, cereal yield, and the ONI. Findings show that WEPSI can be used for agricultural drought assessments.

Evaluating the predictability of eight Atmospheric-Oceanic signals affecting Iran’s Droughts, employing intelligence based and stochastic methods

The large-scale atmospheric-oceanic phenomena are among the main effective factors in the droughts in the Middle East, especially in Iran. Since these effects are usually delayed, their relevant signals can be useful for predicting droughts. As a result, the provision of a precise prediction of these signals can be efficient in increasing the drought prediction prospect. The current study predicts 8 cases of the most effective oceanic signals on the droughts which have been investigated in Iran. To do so, the problem-solving method with the time series prediction approach is based on the two model types intelligence-based (including multilayer perceptron [MLP] and support vector machine [SVM]) and stochastic (including Autoregressive Integrated Moving Average [ARIMA]) has been used. The model's input for each index included the time lags of the same index itself, which was determined by the autocorrelation function. Based on the evaluation criteria, the results were indicative of the weak predictability of the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO), while the Extreme Eastern Tropical Pacific sea surface temperature (Niño [1 + 2]), East Central Tropical Pacific sea surface temperature (Niño [3 + 4]), and Oceanic Niño Index (ONI) were predicted with very good accuracy, and there is a high overlap between their predictions and observations (95.9 % < R2 < 99.3 %). In the extreme events also, the rate of normalized forecasting error for Niño (1 + 2), Niño (3 + 4), and ONI were in the medium (20–30 %), good (10–20 %), and excellent (0–10 %) ranges, respectively. The comparison between the models also indicates a partial superiority of the ARIMA stochastic model over the SVM and MLP models. The overall results of the study are indicative of the applicability of the predictions of the three mentioned indices as the inputs to increase precipitation and drought forecasting prospects in Iran (as well as all regions affected by them); which have the research value for further studies in terms of drought forecasting.

Did Schwabe cycles 19–24 influence the ENSO events, PDO, and AMO indexes in the Pacific and Atlantic Oceans?

The sea surface temperature (SST), anomalies (SSTA), Oceanic Niño Index (ONI), and Multivariate ENSO (MEI) index in El Niño 1 + 2 and El Niño 3.4 regions, Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO), as well as the meteorological parameter Southern Oscillation Index (SOI) were cross correlated to sunspot counts (SS) from cycles 19 to 24 (1954–2019). Over the 1954–2019 period, the SS or Schwabe cycles did not show strong cross-correlation coefficients (cc-ρ) with values falling between 0.063 and 0.100 ( p < 0.05). It seems that the Total Solar Irradiation (TSI) constant variability (±0.1%, ±1.361 W m −2 ) due to the SS cycles balanced out throughout the whole period. The cc-ρ coefficients for SST and SSTA versus each individual SS cycles were between 0.100 and 0.200 with lag times between maxima of each being from a few to 48 months. The ONI showed a cc-ρ < 0.1, while MEI reached up to 0.2 through all SS cycles. The slope of the cc-ρ changed from negative to positive over 6–12 months periods, with peaks in slope change occurring somewhere between 2 and 3 years. The SOI cc-ρ varied by around 0.200 through cycles 19–21, but for SS 22–24 it was not noticeable. Interdecadal indexes PDO and AMO showed cc-ρ of up to 0.283; with a possible association of 8%, with a lag time of 1–3 years. During the ascending and descending phases of each SS cycle, the cc-ρ were 0.288 and − 0.233, respectively for SST in El Niño 3.4 region, but in El Niño 1 + 2 were negligible. The ONI and MEI showed cc-ρ up to 0.448 and 0.480 respectively with lag times of 1–15 months in ascending phase of the SS cycle. The SS vs SOI had cc- ρ up to +0.420 to −0.567 in the ascending phases, while PDO and AMO registered cc-ρ up to 0.417 and − 0.491. AMO appeared systematically associated with SS cycles from 10.0 to 30.2% during descending and ascending phases, respectively. Compiled SS counts for all the ascending and descending phases of the SS cycles, gave a clear spectral coherence (quasi sine-function distribution); for SST and SSTA, lag times of 9–48 months were observed with inverse and direct linear relationship, and peaks of −0.38 and 0.39, respectively. The ONI and MEI with SS counts have similar cc-ρ values: 0.245 and 0.387. Around 6–15% of the ONI and MEI can be explained by SS during ascending and < 4% in descending phases. It seems that over the relatively short time scales of SS cycles, either on their initial ascending or final descending phases, the studied indexes seem to be influenced. Even though it was difficult to elucidate the physics behind the observed cross-correlations, these results can be used to help improve understanding and aid the predictions of ENSO, PDO and AMO oceanographic events in the Pacific and Atlantic oceans. • Sea surface temperature is affected by sun energy that varies in time due to astronomical factors and sun internal activity • The sun activity can be estimated by sun spots (SS) • Inter-annual El/La Niño/Niña as well as decadal processes should be affected by SS • It was found weak to moderate cross-correlation between SS and oceanographic indexes from 1954 to 2019 • This fact should be considered to improve skill modelling forecasts of such indexes

Recurrent dinoflagellate blooms in the South-eastern Arabian Sea - A preliminary assessment with focus on Ornithocercus magnificus blooms along the nearshore waters off Dakshina Kannada, South-west coast of India

Frequent bloom incidences of the dinoflagellate Ornithocercus magnificus Stein, 1883 along the surface waters off South-eastern Arabian Seas (SEAS) have been reported since 2015 and specifically during the years 2015, 2019 and 2020. All these blooming incidences coincided with the strong El Nino phases in the Indian Ocean. The present study addressed the bloom (1.65 x 106 cells l-1) of this species in the nearshore waters (7 m depth) off Dakshina Kannada (Surathkal and Chitrapur) on 11.02.2016 which occurred during pre-monsoon period and corresponding to the super El Nino year 2015/2016. A detailed evaluation of the physico-chemical characteristics and phytoplankton abundance as well as community structure was made, concurrent to the three phases viz., pre-bloom, bloom and post-bloom periods in this region. The study revealed that the bloom occurred in high saline (35.0±0.91 PSU), well oxygenated (7.414±0.823 mg l-1) and nitrogen limited waters (oligotrophic conditions) and had a positive correlation with oceanic nino index (ONI; rs= 0.790, p&lt;0.001). Some of the O. magnificus (3%) harboured ectosymbionts probably cyanobacteria in their cingulum in response to the beginning of a stratified oligotrophic condition in this region. The water quality was fair during the bloom period with no conspicuous discolouration of the surface waters. The present study also attempted to evaluate the influence of the increasing frequency of the O. magnificus blooms on the oilsardine fishery along the SEAS. Keywords: Cyanobacteria, Dinoflagellate, Ectosymbionts, Nitrogen limited waters, Pre-monsoon season, SEAS, Sea surface temperature (SST), Oilsardine

Drought Monitoring Using MOWCATL Data Mining Algorithm in Aras Basin, Turkey

Drought is a natural phenomenon that occurs frequently and has some adverse effects on the ecosystem and humanity. Determination of drought beforehand is vital for optimal management of water resources. Many different methods have been developed to detect drought. Sequential association analysis is used for the data series analysis containing time information and is one of the methods used to determine the drought. A correlation can be established between the values taken by the data at different times when determining association rules with this method. The primary purpose of this study is to determine the sequential association patterns between precipitation and climate oscillation index for Aras Basin. The Aras basin is a region where irrigation and animal husbandry are common. Today, many dams and hydroelectric power plants, together with the increasing population, meet the water and energy needs. A possible drought event in this region will adversely affect the living things in the basin. Therefore, the study focused on this basin. Finding sequential associations between precipitation and climate oscillation index can determine the temporal correlations between these parameters and specifically detect drought. The MOWCATL (Minimal Occurrences with Constraints and Time Lags) algorithm was used to detect sequential associations, and the J-measure was used to evaluate the patterns in the study. Sequential association patterns were determined by applying this method to the precipitation data obtained from 6 meteorology stations in the Aras basin. AO (Arctic Oscillation) Index, MEI (Multivariate ENSO) Index, NAO (North Atlantic Oscillation) Index, Oceanic Niño Index (ONI), PDO (Pacific Decadal Oscillation) Index, PNA (Pacific/North American), and SOI (Southern Oscillation Index), followed by the 1, 3, 6 and 12-month Agricultural Standardized Precipitation Index (a-SPI) were used in sequential association. The study results revealed that the antecedent parameters were ineffective in detecting arid conditions in Ardahan and Doğubeyazıt stations, and they were influential on drought conditions, especially in a-SPI-3 and a-SPI-12 month periods at other stations. Although the altitude and geographical features are different, similar climatic patterns have been detected in some stations. As a result, it has been determined that climatic oscillations generally bring about typical situations in terms of drought for the Aras Basin.

Allometry and Individual Growth of the Temperate Pacific Sardine (Sardinops sagax) Stock in the Southern California Current System

We analyzed individual growth and body mass allometry of the temperate stock of Sardinops sagax from samples collected on commercial fleet during 2005 to 2014 in the southern California Current System. Ages were estimated on otolith growth ring reading. Age (years) and standard length (SL; mm) data were fitted using four growth models: von Bertalanffy (VBM), Gompertz (GM), logistic (LM), and Schnute (SM) models. Parameters of each model were calculated by the maximum likelihood method using Akaike information criterion and Akaike’s weight (WAICi) to select the model with the greatest plausibility. Additionally, the interannual growth performance (φ′) variation was described. Body mass allometry was established from the relationship between total body mass (TM; g) and standard length. The sardine samples ranged in size from 114 to 226 mm SL and in weight from 20 to 192 g TM; ages varied from 0.5 to 6.0 years. The allometric parameter (b) was estimated to be 3.155 (3.154–3.156), showing a hyper-allometry growth type. On the basis of raw data, we found that the LM model fit the original data better (WAICi ~35%). When models were fitted to data bolstered with simulated values, the VBM model obtained the greatest weight (WAICi = 60.9%), but the LM model had null plausibility. Consequently, we determined that the Pacific sardine growth pattern was best described by the VBM model, which appears to be more robust against biased data. The φ′ showed interannual variation (2.13–2.51), with an average of φ′ = 2.25. An apparent inverse relationship was observed between φ′ and the Oceanic Niño Index (ONI) or the Pacific Decadal Oscillation (PDO) and the apparent positive relationship between Upwelling Index (UI) and φ’.

Extreme anomalies under El Niño and La Niña developing tendencies restrain subsequent severe typhoons in the western North Pacific

Forecasting typhoon features over a few upcoming months is necessary for planning disaster prevention and predicting the drought/flood situation. However, the current methods are more suitable for forecasting the interannual to interdecadal variation trends or demanding huge computing resources and are unsuitable for the operation of short-term climate forecasts. In order to devise other effective approaches, the correlation between the Oceanic Niño Index (ONI) in January and the frequency of severe typhoons (STYs) during 1959–2018 was analyzed for the two scenarios when the ONI trend during the previous year was higher and lower than zero. The reason for this classification is that the air‒sea conditions were different when the oceanic phenomena developed toward an El Niño or La Niña phase during the previous year, and the results of these trends affect the subsequent features of atmospheric dynamic factors favorable for TC development (relative vorticity and vertical velocity). The extreme anomalies under both the increasing and decreasing tendencies of ONI in January typically retrained the growth of STYs, as the subsequent decreasing ocean heat anomaly of the previous year was accompanied by a negative vorticity anomaly and downward vertical velocity anomaly in the lower atmosphere and vice versa. Results from the forecast test show that the accuracy of the new approach was acceptable and somewhat higher than that of the current methods. Thus, these results can assist in developing a practical approach for typhoon forecasts.

Time-Series Model for Climatological Forest Fire Prediction over Borneo

Areas covered by tropical forests, such as Borneo, are vulnerable to fires. Previous studies have shown that climate data is one of the critical factors affecting forest fire. This study aims to predict the forest fire over Borneo by considering the temporal aspects of the climate data. A time seriesbased model, Long Short-Term Memory (LSTM), is used. Three LSTM models are applied: Basic LSTM, Bidirectional LSTM, and Stacked LSTM. Three different experiments from January 1998 to December 2015 are conducted by examining climate data, Oceanic Nino Index (ONI), and Indian Ocean Dipole (IOD) index. The proposed model is evaluated by Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and correlation number. As a result, all models can capture the spatial and temporal pattern of the forest fires for all three experiments, in which the best prediction occurs in September with a spatial correlation of more than 0.75. Based on the evaluation metrics, Stacked LSTM in Experiment 1 is slightly superior, with the highest annual pattern correlation (0.89) and lowest error (MAE= 0.71 and RMSE=1.32). This finding reveals that an additional ONI and IOD index as the prediction features would not improve the model performance generally, but it specifically improves the extreme event value.

Dynamic multi-dimensional identification of Yunnan droughts and its seasonal scale linkages to the El Niño-Southern Oscillation

Study regionYunnan Province, China. Study focusYunnan Province (YP) is affected by frequent droughts that severely affect local agriculture and the ecological environment. Therefore, the identification of droughts and an analysis of their driving factors are of significant importance for mitigating local drought losses and guiding agricultural practices. This study identified the spatiotemporal distribution and dynamic changes in drought events over YP (period 1961–2018) using the severity-area-duration method. The impact of El Niño-Southern Oscillation (ENSO) on seasonal droughts and their lag period were also quantified by employing the sliding correlation coefficient and cross-wavelet analysis method. New hydrological insights for the region74 drought events were identified during 1961–2018 in YP, which were mainly short-duration that occurred in the 1980 s and 2000 s, and most drought centers are located over the northern and eastern parts of YP. We found that a significant correlation and different lag periods exist between Oceanic Niño Index (ONI) and seasonal precipitation in YP. Spring droughts mainly occurred in El Niño years during the 1980 s and the 1990 s, whilst winter droughts mainly occurred in La Niña years during the 1990 s and the 2000 s with a lag period of up to 12 months.

Spatial-Temporal Variability of Vegetation Health During El Niño 2015/2016 and La Niña 2017/2018: A Case Study of Sarawak

The El Niño incident in 2015/2016 resulted in a 30% decrease in Gross Domestic Product (GDP) mainly from the agricultural sector. This is due to the El Niño incident which contributed to the prolonged drought for 3 months. This prolonged drought disaster has caused the deterioration of plant health due to a decrease in the amount of rainfall. This study examines and analyzes the spatial-temporal dynamics to identify and delineate the vegetation stress zones in Sarawak. In achieving the objectives of this study Vegetation Health Index (VHI) has been used in studying the changes in monthly space due to changes in Oceanic Niño Index (ONI). This study only focuses on the two occurrences of El Niño in 2015/2016 and La Niña in 2017 and 2018 because the Visible Infrared Imaging Radiometer Suite (VIIRS) data only started operations in 2013 until now. The results of this study offer the discovery of the effect of La Niña on plant health compared to previous studies that only focus on the effects of El Niño (Drought) on plant health. Indirectly, this study can provide information on which areas of the district are severely affected by the occurrence of La Niña and El Niño on plant health.

Passive tracer advection in the equatorial Pacific region: statistics, correlations and a model of fractional Brownian motion

Abstract. Evaluating passive tracer advection is a common tool to study flow structures, particularly Lagrangian trajectories ranging from molecular scales up to the atmosphere and oceans. Here we report on numerical experiments in the region of the tropical Pacific (20∘ S–20∘ N), where 6600 tracer parcels are advected from a regular initial configuration (along a meridional line at 110∘ W between 15∘ S and 15∘ N) during periods of 1 year for 25 years altogether. We exploit AVISO surface flow fields and solve the kinematic equation for passive tracer movement in the 2D advection tests. We demonstrate that the strength of the advection defined by mean monthly westward displacements of the tracer clouds exhibit surprisingly large inter- and intra-annular variabilities. Furthermore, an analysis of cross-correlations between advection strength and the El-Niño and Southern Oscillation (SOI) indices reveal a significant anticorrelation between advection intensity and ONI (the Oceanic Niño Index) and a weaker positive correlation with SOI, both with a time lag of about 3 months (the two indices are strongly anticorrelated near real time). The statistical properties of advection (time-dependent mean squared displacement and first passage time distribution) suggest that the westward-moving tracers can be mapped into a simple 1D stochastic process, namely fractional Brownian motion. We fit the model parameters and show by numerical simulations of the fractional Brownian motion model that it is able to reproduce the observed statistical properties of the tracers' trajectories well. We argue that a traditional explanation based on the superposition of ballistic drift and a diffusion term yields different statistics and is incompatible with our observations.

Remote Impact of the Equatorial Pacific on Florida Current Transport

AbstractSatellite and in‐situ measurements are used in this study to investigate a possible link between the Florida Current transport (FCT) and temperature changes in the equatorial Pacific. Consistent with the geostrophic nature of the current, the FCT variability shows good correspondence with the changes in sea surface height differences (∆SSH) between the eastern (SSHeast) and western (SSHwest) sides of the Florida Straits. While the variability of SSHwest is mostly associated with seasonal and shorter timescale fluctuations of ∆SSH and FCT, changes in SSHeast are strongly related to the interannual variability of ∆SSH and FCT. A significant correlation is found between the FCT and the Oceanic Niño Index (ONI) on interannual timescales, which explains 21% of the interannual FCT variance. The connection of ONI with FCT is through its impact on SSHeast, associated with the anomalous convergence/divergence in the Caribbean region and the Bahamas forced by ONI‐induced wind stress curl changes.

Rapid surface water warming and impact of the recent (2013–2016) temperature anomaly in shallow coastal waters at the eastern entrance of the Gulf of California

Anthropogenically-induced global warming is increasing surface seawater temperatures (SST) worldwide. Furthermore, the Northeastern Tropical Pacific is affected by seasonal, interannual (El Niño – Southern Oscillation, marine heatwaves), and decadal variabilities (Pacific Decadal Oscillation). Here we analyzed a long-term (1973–2016) SST time-series from the Mazatlán coastal zone, at the eastern entrance of the Gulf of California (Mexico) to record the warming trend and to examine the effect of the 2013–2016 North Pacific SST positive anomaly. This SST time-series can be useful to better understand the interplay of surface water masses in the region (Tropical Surface Water, Gulf of California Water, and California Current Water) and their transport mechanisms. Variability analysis was focused on 2007–2016, for which a 10-year climatology was established. During this period, two La Niña and El Niño events, and a regional marine heatwave occurred in the Northeast Pacific. There was a significant positive correlation between the Oceanic Niño Index (ONI) and SST anomalies. La Niña events caused large negative anomalies (up to 4 °C) in maximum SST, and El Niño events caused large positive anomalies (up to 7 °C) in the minimum SST. The 2013–2014 regional marine heatwave was preceded by a previously unreported anomalous regional warming at the entrance of the Gulf of California (2012–2013), which caused positive anomalies of 4 °C in minimum SST. Since then, each winter showed increasing minimum SST anomalies until reaching a 7 °C maximum in 2016. Minimum SST positive anomalies were caused by i) the inhibition of coastal upwelling of cold waters, and ii) a limited penetration of California Current cold waters. For the past 40 years, we estimated a mean warming trend of 0.57 ± 0.01 °C per decade in the study area, which is larger than the global ocean increasing trend. This relatively fast warming rate could be caused by i) local effects caused by shallowness and coastal dynamics, ii) an increasing influence of warmer Tropical Surface Water transported by the Mexican Coastal Current, iii) the transport and upwelling of warmer waters from the Western Pacific Warm Pool during El Niño events, and iv) global warming, as increasing SST can induce stronger and longer-lasting thermoclines, especially in regions commonly affected by upwelling events. If this trend persists, a simple linear extrapolation would indicate that the mean SST from 1977 to 2100 would increase by 7 °C. This extrapolation should be taken with caution, but such an increase would have profound effects on the coastal ecosystems, so possible adaptation strategies merit urgent attention by scientists and coastal zone managers to dynamically adapt to coastal global warming and marine heatwaves. This methodology can be used in other coastal zones worldwide where enough field and satellite data are available.

Failure to advance migratory phenology in response to climate change may pose a significant threat to a declining Nearctic-Neotropical songbird.

Populations of long-distance migrants that breed in seasonal habitats can be significantly impacted by climate change. We examined the migratory and breeding phenologies of the cerulean warbler (Setophaga cerulea), a declining long-distance Nearctic-Neotropical migrant that breeds in deciduous forests of Indiana. Our primary objectives were to determine temporal trends in cerulean warbler migratory timing, and to identify climate variables that explain variation in this species' migratory and breeding phenologies. We reviewed trends in cerulean warbler first arrival to Indiana from 1982 to 2019, and compared them to several explanatory climate variables: spring temperature, growing degree days (GDD), North Atlantic Oscillation (NAO) index, and Oceanic Niño Index (ONI). We also compared the timing of cerulean warbler first lay dates from 2012 to 2019 with the aforementioned climate variables and annual spring precipitation. Cerulean warblers exhibited a minimal advance in first arrival timing (≤4 days in 38 years). Arrival timing was best predicted by GDD and a null model, but trends in GDD indicate that spring warming in Indiana has advanced by a greater margin, approximately 14 days. Climate variables did not predict first lay timing better than a null model. Springtime in Indiana is occurring earlier, but cerulean warblers are advancing their migratory timing to a much smaller degree. This failure to adapt may have a detrimental effect on warbler populations if it results in an asynchronization of important biological timings between them and their prey. Further studies of cerulean warbler breeding and prey phenologies are necessary to determine how climate change is impacting this species' reproductive success.

Sea surface temperature trend analysis by Mann-Kendall test and sen’s slope estimator: a study of the Hai Phong coastal area (Vietnam) for the period 1995-2020

Based on the Mann-Kendall test and Sen’s slope method, this study investigates the monthly, seasonal, and annual sea surface temperature (SST) trends in the coastal area of Hai Phong (West of Tonkin Gulf) based on the measurements at Hon Dau Station from 1995 to 2020. The results show a sea surface warming trend of 0.02°C/year for the period 1995-2020 (significant level α = 0.1) and of 0.093°C/year for the period 2008-2020 (significant level α = 0.05). The monthly SSTs in June and September increased by 0.027°C/year and 0.036°C/year, respectively, for the period 1995-2020, and by 0.080°C/year and 0.047°C/year, respectively, for the period 2008-2020. SST trends in winter, summer, and other months were either different for the two periods or not significant enough. This may be due to the impact of ENSO, which caused interannual SST variability in the Hai Phong coastal with two intrinsic mode functions (IMF) signals a period of ~2 (IMF3) and ~5.2 years cycle (IMF4). A combination of these signals had a maximum correlation of 0.22 with ONI (Oceanic Niño Index) delayed by 8 months. ENSO events took ~8 months to affect SST at Hai Phong coastal area for 1995-2020 and caused a variation of SST within 1.2°C.

Evaluation of environmental degradation on Biomes in Brazilian Northeastern

In recent years, due to irregular rainfall, several regions across the globe have suffered from some environmental degradation. Depending on the degree of intensity, they can lead to a series of socioeconomic problems, both regional and national. One of the most emblematic types of degradation today is the desertification development process, resulting in a combination of anthropic, climatic, and/or environmental factors. In an attempt to understand the behavior of these impacts, this work evaluated the degradation process based on the Aridity Index (AI) on biomes located in the Brazilian Northeast (Caatinga, Atlantic Forest, Amazon, and Cerrado). The AI consists of the relationship between evapotranspiration (in this case, the Thornthwaite estimation method) and precipitation. The data used correspond to the product Climatologies at high resolution for the earth's land surface areas (CHELSA), which have a spatial resolution of 1km x 1km and temporal resolution on a monthly scale, from 1979 to 2013. In addition, it was found that the AI behavior during years of the El Niño-Southern Oscillation (ENSO) warm phase, the El Niños. The identification of El Niño episodes was based on the Oceanic Niño Index (ONI) climate proxy, characterized by the Equatorial Pacific region known as the Niño 3.4 region. The years 1983, 1993, 1998, and 2012 were selected. AI results point out different behaviors between the biomes, mainly over the south of the northeastern Cerrado and the Caatinga, motivated by the strong variability of rainfall in the respective regions. The Caatinga stands out, which exhibited a large part of its area was classified as arid (AI between 0.05 – 0.20) and hyper-arid (AI &lt; 0.05), mainly in 2012. The Amazon and biomes, on the other hand. Atlantic Forest has areas classified as semi-arid (AI &lt; 0.5). El Niño had a reduction in the AI values, motivated by the long periods of drought and irregular rainfall that negatively impacted the semi-arid of the Northeast region.

Feature Selection and Spatial-Temporal Forecast of Oceanic Niño Index Using Deep Learning

El Niño-Southern Oscillation (ENSO) is a climate phenomenon caused due to irregular periodic oscillation in easterly winds and sea surface temperature (SST) over the tropical Pacific Ocean. ENSO is one of the main drivers of Earth’s inter-annual climate variability, which causes climate anomalies in the form of tropical cyclones, severe storms, heavy rainfalls and droughts. Due to the impact of ENSO on global climate, forecasting ENSO is of great importance. However, forecast accuracy of ENSO for a lead time of one year is low. ENSO events are forecasted through Oceanic Niño Index (ONI), which is the three-month running mean of SST anomalies over the Niño 3.4 region (5∘N-5∘S, 120∘W-170∘W). Features, such as SST, sea level pressure, zonal wind speed and meridional wind speed that contribute in determining ONI are mapped on spatial or geographical grids, where each spatial or geographical grid represents the values of one feature at a snapshot. Juxtaposing the spatial grids of all features creates a layered map at a snapshot. The layered spatial feature map is constructed at different snapshots, and they all are fed to the CLSTM to forecast ONI at lead times of 1, 3, 6, 9 and 12 months. This study employs backward stepwise feature selection based on generalization accuracy to find the most effective features. SST showed to be the best feature for forecasting ONI. Experiments showed that the CLSTM outperforms Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Standard Neural Network (SNN) in terms of coefficient of determination ([Formula: see text]), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). More specifically, an improvement in [Formula: see text] values by 27.6%, 20.9%, 25% and 15.2% over CNN is observed for lead times of 3, 6, 9 and 12 months, respectively.

Analysis of Relationships Between ENSO Events and Atmospheric Angular Momentum Variations

AbstractThe El Niño–Southern Oscillation (ENSO) event has a long incubation process, during which the interannual variation of length‐of‐day (LOD) and the atmospheric angular momentum (AAM) series will be quickly affected due to the interaction between sea and air. Based on the comparisons between filtered interannual LOD variation, the AAM, the Oceanic Niño Index (ONI), and the Southern Oscillation Index (SOI) for the period of January 1953 and December 2013, the relationships among them and ENSO are studied. The results demonstrate that AAM changes and SOI/ONI series have similar waveform structures and trends. Interannual LOD variations, atmospheric LOD excitations, and ENSO indices such as the SOI and ONI are well correlated as a consequence of angular momentum conservation. AAMs can be accurately modeled at present, taking ESMGFZ as an example, its sampling interval can reach 3 hr, and can realize accurate prediction in the next 90 days. Consequently, AAM series also can be used as an index for ENSO events and has its own advantages compared with SOI/ONI.

Investigating El Nino Southern Oscillation as the main driver of forest fire in Kalimantan

Strong El Nino events have been identified as major factors contributing to the forest fire in Indonesia. This is due to El Nino Southern Oscillation (ENSO) variability has a distinct connection with tropical precipitation, mainly in Indonesia. El Nino years are typically drier, while La Nina events generally increase precipitation in Indonesia. Although very strong El Nino events have been connected with massive forest fires, fire continue to occur during the other phases of ENSO: La Nina and normal. Here, the research reports a time series of monthly counted fires in Kalimantan between the period 2001-2020 from MODIS fire hotspot and MODIS Burned Area products. The region is divided into three categories, Primary Intact Forest, Primary Degraded Forest and Outside Forest/ Deforested area. This categorization validates the location of the fire. Our results show that in general wildfires in Kalimantan follow a similar temporal pattern with Oceanic Nino Index (ONI), with several anomalies. If ONI is high, wildfires are more intense and vice versa. The wildfire appears almost every month and increases drastically in June-October of El Nino years. However, the proportion of wildfires in Primary Intact Forest are tiny and insignificant. The primary intact forest fire only appears in July-October and have a different pattern with wildfires in general. In conclusion, wildfires are highly correlated with El Nino but limited in primary intact forests. The fires dominantly appear in the deforested area, about 80%. The rest 20% are in degraded forest and only less than 1% in primary intact forest. Keywords: Deforested, degraded forest, ENSO, forest fire, ONI, primary intact forest